The Bottlenecks of Adopting AI in the Healthcare Industry
Last Updated January 2, 2023
Artificial intelligence (AI) has been an advantage for many sectors, including the healthcare industry. It has the potential to save lives and enhance the health of millions of patients worldwide.
Through precise decision-making tools, AI can increase hospital’s productivity and expand patient’s access to care. AI is educating a computer model using complex and large data sets. The model learns from this data in a training process to build its ability to make decisions or predict outcomes when presented with new data.
Several bottlenecks have hampered AI implementation in the healthcare industry. From data privacy and security concerns to limited healthcare resources and regulatory barriers, the path to widespread AI implementation has its challenges. In this article, we dig deep into these bottlenecks and discuss ways to overcome them to unlock the full potential of AI.
Data Quality and Availability
One of the major bottlenecks to adopting artificial intelligence (AI) in healthcare is data quality and availability, which refers to obtaining high-quality data in sufficient quantities to train AI models. They need to be trained on large, high-quality datasets to be effective.
However, data can be challenging to obtain due to several reasons:
- Healthcare data is often siloed, with different types stored in other systems and formats, making it difficult to access and integrate data to train AI models.
- Data can have errors and inconsistencies that make it challenging to use for AI training.
- There may be privacy or security concerns that limit the availability of healthcare data for AI training.
Hence, the data quality and availability challenge can be a significant bottleneck to adopting AI in healthcare
Data Privacy and Security
Another bottleneck to adopting artificial intelligence (AI) in healthcare is data privacy and security, which is the need to protect patients’ privacy and ensure data security using AI.
Healthcare data is sensitive and must be protected to maintain patients’ privacy. Ensuring the confidentiality and security of healthcare data can be challenging when using AI.
One concern is the potential for data breaches, where unauthorized individuals may gain access to sensitive healthcare data.
This can lead to various repercussions, including financial fraud, identity theft, and harm to patient privacy. In addition, there may be concerns about using healthcare data for purposes other than patient care, such as marketing or research.
To resolve these concerns, healthcare organizations must implement strict data security protocols and work with external partners to ensure that data is stored and transmitted securely.
Ensuring the privacy and security of healthcare data is non-negotiable for the successful adoption of AI in healthcare industry.
Regulatory barriers can also be a significant bottleneck to adopting artificial intelligence (AI) in healthcare which pertains to the regulatory approval processes and other barriers that may be required to apply new technologies in healthcare, in this case, AI.
In the healthcare industry, new technologies often require clinical trials and other regulatory approvals before they can be widely adopted. This can be expensive and time-consuming and slow down AI adoption in healthcare.
In addition, there may be concerns about the safety and effectiveness of AI systems, particularly when they are used to make clinical decisions.
To address these concerns, healthcare organizations must work closely with regulatory agencies and conduct extensive testing to ensure the safety and effectiveness of their AI systems. This can be a significant hurdle for smaller organizations with limited resources.
Overall, regulatory barriers can play a major role in hindering the adoption of AI in healthcare.
Limited Healthcare Resources
We need more resources to ensure the adoption of artificial intelligence (AI) in healthcare.
Implementing AI systems can be resource-intensive, requiring funding, expertise, and time. These resources may be limited, particularly in resource-limited settings.
Let’s try to understand this with an example, smaller hospitals or clinics may need more funding or expertise to build and maintain AI systems.
In addition, implementing AI systems may require significant upfront costs, which can be challenging for organizations with limited budgets.
To overcome this bottleneck, healthcare organizations may need to seek external funding or partnerships to bring in specialized AI expertise. They may also need to prioritize implementing AI systems that will provide the greatest return on investment.
Resistance to Change
Resistance to change is another bottleneck to adopting artificial intelligence (AI) in healthcare.
There may be resistance to adopting AI in healthcare from healthcare professionals and patients. Some people may be concerned about the impact of AI on jobs and the potential for errors.
Healthcare professionals may be reluctant to adopt new technologies, mainly if they are unfamiliar with them or have concerns about their effectiveness.
Patients may also be hesitant to use AI-powered systems, mainly if they are unfamiliar with the technology or have concerns about the potential for errors.
Healthcare organizations may need to invest in training and education programs to combat this bottleneck. As a result, staff will better understand AI’s advantages and how we can enhance patient care. They may also need to work to communicate the benefits of AI to patients and address any concerns they have about the technology.
Real-Life Case Study in Healthcare Industry
Let’s have a look at a real-life case study of Northwell Health. Northwell Health is a vast healthcare system based in New York. In 2018, the organization started to explore the use of artificial intelligence (AI) to improve patient care and reduce costs. However, they quickly encountered several bottlenecks to AI adoption.
One major challenge was the quality and availability of data. Northwell Health had large amounts of siloed and unformatted data. The organization invested in data infrastructure to overcome this bottleneck and worked with external partners to build a centralized, structured data repository.
Another challenge was the presence of regulatory barriers. To overcome this bottleneck, Northwell Health worked closely with regulatory agencies and conducted extensive testing to ensure the safety and effectiveness of their AI systems.
Finally, there was some resistance to change from healthcare professionals and patients. To tackle this bottleneck, Northwell Health invested in training and education programs to help staff understand the benefits of AI and how implementing it could improve patient care. They also worked to communicate the benefits of AI to patients and address any concerns they had about the technology.
Through data infrastructure improvements, regulatory compliance, education, and outreach, Northwell Health overcame the bottlenecks to AI adoption and successfully implemented several AI-powered systems.
These systems have enabled the organization to improve patient care, reduce costs, and streamline operations.
In conclusion, applying artificial intelligence (AI) to healthcare has the potential to transform the sector entirely. But as mentioned above, many bottlenecks are slowing down the adoption of AI in healthcare. The road to widespread AI adoption in healthcare may be bumpy. However, by overcoming these roadblocks, we can get closer to a time when AI will play a significant role in healthcare.
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Naima is a skilled and experienced content writer, with a passion for creating high-quality, well-researched articles and blog posts. With her strong writing skills and attention to detail, Naima is able to craft engaging and informative content that resonates with readers.
In addition to her work as a content writer, Naima is also highly interested in technology and artificial intelligence and is always looking for ways to stay up-to-date on the latest trends and developments in these fields.